Bayesian Variable Selection in Regression Models Using the Laplace Approximation

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Naghizadeh Sima

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Journal Paper

Paper Information

Journal: Journal of Data Science and Modeling Year:2022 | Volume:1 | Issue:1 Page(s): 171-188

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Title

Bayesian Variable Selection in Regression Models Using the Laplace Approximation

Author(s)

Naghizadeh Sima | Issue Writer Certificate

Keywords

Bayesian Variable Selection

Mixture of Priors

Bartlett’s Paradox

Information Paradox

Empirical Bayesian analysis

Abstract

The Bayesian Variable Selection analysis is widely used as a new methodology in air quality control trials and generalized linear models. One of the important and, of course, controversial topics in this area is selection of prior distribution of unknown model parameters. The aim of this study is presenting a substitution for Mixture of Priors which besides preservation of beneﬁts and computational eﬃciencies obviate the available paradoxes and contradictions. In this research we pay attention to two points of view,empirical and fully Bayesian. Especially, a Mixture of Priors and its theoretical characteristics is introduced. Finally, the proposed model is illustrated with a real example.

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